Abstract
Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the precollected data of model weather radar echo patches. Second, the most relevant subdictionaries are adaptively select for each low-resolution echo patches during the spare coding. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics.
Highlights
China Generation Weather Radar (CINRAD) have been widely applied in operational research and forecast on medium-scale and short-duration strong weather phenomena
We propose an adaptive sparse domain selection (ASDS) algorithm for superresolution reconstruction of weather radar data, which learn a compact dictionary from a precollected dataset of example weather radar echo patches by using the principal component analysis (PCA) algorithm, the most pertinent subdictionaries are adaptively selected for each low-resolution echo patches during the sparse coding
To further validate the effectiveness of ASDS under different weather condition, the statistical comparison is made in terms of quantitative evaluation metrics (PSNR, structural similarity (SSIM))
Summary
China Generation Weather Radar (CINRAD) have been widely applied in operational research and forecast on medium-scale and short-duration strong weather phenomena. (1) An efficient superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article (2) Two adaptive regularization terms are introduced to improve the reconstruction effect of the edge and intense echo information of the radar echo (3) e proposed ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics e remaining part of the article proceeds as follows. E research article [10] proposed an interpolation method to improve the resolution of radar reflectivity data, which effectively uses the hidden Markov tree (HMT) model as a priori information to well capture the multiscale statistical characteristics of radar reflectivity data in small-scale strong precipitation condition. Methodology is section describes the research methodology proposed for the subject title
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